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119 行
5.5 KiB
119 行
5.5 KiB
# # Unity ML-Agents Toolkit
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import logging
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from multiprocessing import Process, Queue
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import numpy as np
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from docopt import docopt
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from .trainer_controller import TrainerController
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from .exception import TrainerError
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def run_training(sub_id, run_seed, run_options, process_queue):
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"""
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Launches training session.
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:param process_queue: Queue used to send signal back to main.
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:param sub_id: Unique id for training session.
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:param run_seed: Random seed used for training.
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:param run_options: Command line arguments for training.
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"""
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# Docker Parameters
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docker_target_name = (run_options['--docker-target-name']
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if run_options['--docker-target-name'] != 'None' else None)
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# General parameters
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env_path = (run_options['--env']
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if run_options['--env'] != 'None' else None)
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run_id = run_options['--run-id']
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load_model = run_options['--load']
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train_model = run_options['--train']
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save_freq = int(run_options['--save-freq'])
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keep_checkpoints = int(run_options['--keep-checkpoints'])
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worker_id = int(run_options['--worker-id'])
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curriculum_file = (run_options['--curriculum']
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if run_options['--curriculum'] != 'None' else None)
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lesson = int(run_options['--lesson'])
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fast_simulation = not bool(run_options['--slow'])
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no_graphics = run_options['--no-graphics']
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trainer_config_path = run_options['<trainer-config-path>']
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# Create controller and launch environment.
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tc = TrainerController(env_path, run_id + '-' + str(sub_id),
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save_freq, curriculum_file, fast_simulation,
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load_model, train_model, worker_id + sub_id,
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keep_checkpoints, lesson, run_seed,
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docker_target_name, trainer_config_path, no_graphics)
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# Signal that environment has been launched.
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process_queue.put(True)
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# Begin training
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tc.start_learning()
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def main():
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try:
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print('''
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▄▄▄▓▓▓▓
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╓▓▓▓▓▓▓█▓▓▓▓▓
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,▄▄▄m▀▀▀' ,▓▓▓▀▓▓▄ ▓▓▓ ▓▓▌
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▄▓▓▓▀' ▄▓▓▀ ▓▓▓ ▄▄ ▄▄ ,▄▄ ▄▄▄▄ ,▄▄ ▄▓▓▌▄ ▄▄▄ ,▄▄
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▄▓▓▓▀ ▄▓▓▀ ▐▓▓▌ ▓▓▌ ▐▓▓ ▐▓▓▓▀▀▀▓▓▌ ▓▓▓ ▀▓▓▌▀ ^▓▓▌ ╒▓▓▌
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▄▓▓▓▓▓▄▄▄▄▄▄▄▄▓▓▓ ▓▀ ▓▓▌ ▐▓▓ ▐▓▓ ▓▓▓ ▓▓▓ ▓▓▌ ▐▓▓▄ ▓▓▌
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▀▓▓▓▓▀▀▀▀▀▀▀▀▀▀▓▓▄ ▓▓ ▓▓▌ ▐▓▓ ▐▓▓ ▓▓▓ ▓▓▓ ▓▓▌ ▐▓▓▐▓▓
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^█▓▓▓ ▀▓▓▄ ▐▓▓▌ ▓▓▓▓▄▓▓▓▓ ▐▓▓ ▓▓▓ ▓▓▓ ▓▓▓▄ ▓▓▓▓`
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'▀▓▓▓▄ ^▓▓▓ ▓▓▓ └▀▀▀▀ ▀▀ ^▀▀ `▀▀ `▀▀ '▀▀ ▐▓▓▌
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▀▀▀▀▓▄▄▄ ▓▓▓▓▓▓, ▓▓▓▓▀
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`▀█▓▓▓▓▓▓▓▓▓▌
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¬`▀▀▀█▓
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''')
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except:
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print('\n\n\tUnity Technologies\n')
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logger = logging.getLogger('mlagents.trainers')
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_USAGE = '''
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Usage:
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mlagents-learn <trainer-config-path> [options]
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mlagents-learn --help
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Options:
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--env=<file> Name of the Unity executable [default: None].
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--curriculum=<directory> Curriculum json directory for environment [default: None].
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--keep-checkpoints=<n> How many model checkpoints to keep [default: 5].
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--lesson=<n> Start learning from this lesson [default: 0].
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--load Whether to load the model or randomly initialize [default: False].
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--run-id=<path> The directory name for model and summary statistics [default: ppo].
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--num-runs=<n> Number of concurrent training sessions [default: 1].
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--save-freq=<n> Frequency at which to save model [default: 50000].
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--seed=<n> Random seed used for training [default: -1].
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--slow Whether to run the game at training speed [default: False].
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--train Whether to train model, or only run inference [default: False].
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--worker-id=<n> Number to add to communication port (5005) [default: 0].
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--docker-target-name=<dt> Docker volume to store training-specific files [default: None].
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--no-graphics Whether to run the environment in no-graphics mode [default: False].
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'''
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options = docopt(_USAGE)
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logger.info(options)
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num_runs = int(options['--num-runs'])
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seed = int(options['--seed'])
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if options['--env'] == 'None' and num_runs > 1:
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raise TrainerError('It is not possible to launch more than one concurrent training session '
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'when training from the editor.')
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jobs = []
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run_seed = seed
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for i in range(num_runs):
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if seed == -1:
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run_seed = np.random.randint(0, 10000)
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process_queue = Queue()
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p = Process(target=run_training, args=(i, run_seed, options, process_queue))
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jobs.append(p)
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p.start()
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# Wait for signal that environment has successfully launched
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while process_queue.get() is not True:
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continue
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